Rehabilitation following a Sports Injury: Do the Motives of Athlete and Athletic Therapist Matter?
Bibliographic record
Abstract
Background: Previous studies indicate motivation is an important consideration impacting return-to-sport following injury. Little is known about the role played by athletes' perceptions of the clinician's motives for providing treatment during rehabilitation.Objectives:The aim of this study was to address the following question: Do the motives expressed by an injured athlete for entering treatment, plus an athletic therapist's motives for providing treatment, matter when rehabilitating a sports injury?Methods: Athletes (N = 97; M age = 20.2±1.9 years; 55.7% female) were randomized to one of four groups which manipulated the salience of intrinsic/extrinsic motives reported by an injured athlete for entering treatment plus an athletic therapist for providing treatment.Dropout and effort put into rehabilitating the injury were measured using a questionnaire at post-test only.Results: Multivariate analysis of variance indicated statistical differences (p< .05) between the groups.Posthoc (Bonferroni) analyses indicated less dropout and more effort were evident when the athlete and athletic therapist engaged in rehabilitating an injury for intrinsic as opposed to extrinsic reasons.Mixed support was evident for the mitigating role of an intrinsically motivated athletic therapist providing treatment to an extrinsically motivated athlete.Discussion: Overall, the results of this study reinforce the importance of understanding the motivational basis for seeking and providing treatment in sports therapy contexts, as well as, the potential role of an athletic therapist's motives in optimizing treatment processes and outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".